A problem in the field of batch processing of applications is these are often long and critical processing which are necessary to ensure proper operation of the application during production, i.e., for users during their work hours. In particular, the execution of batch processing sometimes causes congestion at the IT infrastructure resources, which endangers operation of the application in production. Such a congestion incident is often characterized by saturation of one or more resources on one or more servers and a significant increase in their duration due to congestion of a resource. Due to prolonging of execution time, in some cases it happens that all processing cannot be executed in the allotted time. In some cases, production even has to be discontinued if the processing were not able to be completed in the planned period. In this way, when incidents occur in production, analysis must be conducted to determine the origin. One particular problem relates to the fact that this analysis is difficult, especially when an incident is linked to batch processing because the sole information available is generally what is linked to scheduling mechanisms.
In this context, it is interesting to propose a solution proposing tools for monitoring batch processing and enabling diagnosis identifying the processing likely to be at the origin of congestion incidents which occur in production.